AI in the Warehouse | FORTNA

6 Ways AI May Impact Warehouse Inventory

Today’s artificial intelligence (AI) offers intriguing potential and promise in warehouse operations and distribution networks. The rise in popularity of AI is partly due to the digital transformation that many organisations have been experiencing over the last several decades.

by Darren Jorgenson

Several science fiction films have featured artificial intelligence (AI) becoming self-aware or malfunctioning, from the HAL 9000 in 2001: A Space Odyssey, to Skynet from The Terminator; many of us grew up thinking that AI could be a bad idea.

With Hollywood sentiment and sci-fi aside, today’s AI offers intriguing potential and promise in warehouse operations and distribution networks. In this FORTNA blog, we’ll examine six ways AI may impact warehouse inventory today and in the future.


The rise in popularity of AI is partly due to the digital transformation that many organisations have been experiencing over the last several decades. As organisations continue to automate and adopt technology, they move closer to leveraging the advantages of AI.

The 2023 MHI Annual Industry Report1 listed the stages of digital adoption for warehouse operations:

  • Stage One: Digital Connectivity (AutoID, Internet of Things, sensors and cloud computing)
  • Stage Two: Automation (robotics, wearable and mobile technology, autonomous vehicles)
  • Stage Three: Advanced Analytics (inventory and network optimization, predictive analytics)
  • Stage Four: Artificial Intelligence

The first three stages of adoption lead to artificial intelligence as the final optimisation level. The survey also reported that 75% of companies plan to implement new AI use cases within their supply chain.

There are many business areas where AI could be implemented, but inventory management is one of the main areas within an operation that organisations are investigating.

1. Forecasting
Powerful AI algorithms that absorb historical sales data, current market trends, economic factors, geography, promotional activity and even weather patterns (to name but a few of the multiple considerations) could provide specific time horizon forecasts that allow an operation to make the best informed decisions for planning and execution. The data could further be used to redeploy stock for reorders and stockouts of longer-lead-time products while helping an organisation understand seasonal and promotional demands.

2. Stock Velocity
A recent FORTNA Insight discussed the Pareto Principle, or the 80/20 rule. With regard to inventory, it implies that 80% of sales come from 20% of SKUs. AI could take an operation’s data like sales velocity, turn times, carrying costs and reordering lead times to help an organisation understand which SKUs should be prioritised for slotting and on-floor placement and identify other SKUs that can be stored in less accessible locations.

3. Disruptions
Artificial intelligence has the ability to collect a company’s internal data points and analyse important external data as well. AI could identify potential disruptions from import delays, weather events, natural disasters and geopolitical issues affecting your supply chain. By identifying and alerting management to these factors, organisations could react quickly and avoid a substantial impact on their operations.


4. Third-Party Suppliers
Environmental, Social and Governance (ESG) has grown in importance over the past few years. Part of this movement is increased pressure on organisations to ensure third-party suppliers or vendors conduct business ethically and environmentally while aligning with their organisation’s vision and mission. AI could assist companies in evaluating their vendors on business factors, such as prices, lead time and quality, and monitor them for any ESG violations or concerns, allowing a company to act quickly and avoid any issues.

5. Predictive Maintenance
Using AI in an operation’s maintenance programme is possibly the most accessible entry point for leveraging AI in a distribution environment. An AI-powered predictive maintenance programme could analyse an operation’s spare parts consumption rates, anticipate when parts or controls reach their end-of-life usefulness and alert maintenance teams to monitor or replace them before a failure occurs.

6. Real-Time Data
For a long time, supply chain executives have desired the ability to crunch distribution network’s data and present it thoughtfully and visually. AI could take the data in real-time and populate meaningful dashboards, graphs and tables that empower an operation to take quick and decisive actions and decisions.

When paired with an expert, such as a data scientist, artificial intelligence is a powerful tool to really unlock the value many organisations require.


FORTNA has the industry experience, subject matter experts and a team of dedicated data scientists to assist operations with their digital transformation that could lead to utilising artificial intelligence in the future.

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About the Author


Darren Jorgenson

Practice Lead, Global Strategy

Darren Jorgenson is the Global Strategy Practice Leader for FORTNA and has been in the industry for 20+ years, serving in multiple industries and consulting roles. Darren is a member of the Council of Supply Chain Management Professionals and has been recognised as a Pro to Know by Supply & Demand Chain Executive magazine.